A Development of Object Detection System based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

Sonsilphong, Suphachoke; Sonsilphong, Amornthep; Hormdee, Daranee; Khampitak, Kovit

doi:10.1109/ieecon53204.2022.9741676

Cited by 2 publications

(2 citation statements)

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“…Instead of only tool presence recognition, numerous articles focus on the type of tool available in the surgical procedure with semantic segmentation [46], [102]- [127]. A noteworthy point arises when articles dealing with organ/object segmentation (see Section IV-E) also consider tool segmentation [128], therefore forming another interconnected relation between two different but relevant tasks.…”

Section: ) Tool Segmentationmentioning

confidence: 99%

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Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and Long Short Term Memory (LSTM) networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

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Section: ) Tool Segmentationmentioning

confidence: 99%

“…Similarly, the segmentation of organs [128], [227], [228] and tissues is also well studied task in RAS [171]. The Mask-RCN and CNN based YOLO, U-Net, TernausNet, LinkNet, and SegNet are applied on famous EndoVis Challenge and in-house datasets.…”

Section: E Othersmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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Sutura automatizada para una plataforma robótica de asistencia a la cirugía laparoscópica

Galán-Cuenca,

De Luis-Moura,

Herrera-López

et al. 2024

Rev. iberoam. autom. inform. ind.

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La cirugía laparoscópica es una cirugía mínimamente invasiva que hace reducidas incisiones en el tejido del paciente durante el procedimiento. Una de las tareas más desafiantes dentro de este campo es la sutura realizada con robots. Este artículo se centra en el algoritmo de sutura automatizada por medio de visión y un controlador fuerza-posición para manejar la herramienta dentro del paciente. El algoritmo recoge una primera aproximación donde la herramienta se sitúa en el punto de sutura, pero no ejecuta la entrada y posterior salida de la aguja en la piel. Se usa una plataforma robótica formada por dos brazos manipuladores, uno para manipular un instrumento quirúrgico a través del punto de fulcro y otro intra-abdominal con cámara con dos grados de libertad, para realizar los experimentos de dicho algoritmo. El artículo se compone de un algoritmo de visión para detectar heridas y localizar la ubicación de los puntos de sutura por medio de imágenes de profundidad, un módulo de navegación para generar la trayectoria de la sutura, y un controlador para ubicar la herramienta en dichos puntos sin dañar la pared abdominal del paciente. Por último, tanto el sistema de navegación como el de control se validaron mediante pruebas in vitro.

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A Development of Object Detection System based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

Cited by 2 publications

References 10 publications

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Sutura automatizada para una plataforma robótica de asistencia a la cirugía laparoscópica

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